高维数据变量选择中MCP正则化参数选择研究
MCP Regularization Parameter Selection in High Dimensional Data Variable Selection
DOI: 10.12677/SA.2019.86096, PDF,    科研立项经费支持
作者: 张肖萍, 吴炜明, 王延新*：宁波工程学院，浙江 宁波
关键词: 变量选择；MBIC；MCP；正则化参数；高维数据；Variable Selection； MBIC； MCP； Regularization Parameter； High-Dimensional Data
摘要: 大数据时代，高维数据的变量选择是现代统计的研究热点问题之一。MCP正则化方法是常用的变量选取方法，但MCP正则化方法的优劣取决于能否选取出最优的正则化参数。本文在BIC准则的基础上，提出适用于MCP正则化参数选择的MBIC准则。通过数据模拟及实际应用表明，MCP方法在MBIC准则下能够以更高的概率选择正确的模型，MBIC准则明显优于其它参数选择方法。
Abstract: In the era of big data, variable selection of high-dimensional data is one of the hot topics in modern statistics. The MCP regularization method is a commonly used variable selection method, but the merits of the MCP regularization method depend on whether the optimal regularization parameter can be selected. Based on the BIC criterion of regularization parameter selection, an MBIC criterion is proposed for MCP regularization parameter selection. Through data simulation and practical application, the MCP method with MBIC criterion can select the correct model with higher probability, which is obviously superior to other regularization parameter selection methods.
文章引用：张肖萍, 吴炜明, 王延新. 高维数据变量选择中MCP正则化参数选择研究[J]. 统计学与应用, 2019, 8(6): 852-858. https://doi.org/10.12677/SA.2019.86096

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